Demographic and initial outbreak patterns of COVID-19 in Thailand

被引:0
作者
Pavitra Jindahra
Kua Wongboonsin
Patcharawalai Wongboonsin
机构
[1] Chulalongkorn University,Sasin School of Management
[2] Chulalongkorn University,ASEAN Studies Center
来源
Journal of Population Research | 2022年 / 39卷
关键词
COVID-19; Cluster analysis; Infection pattern; Heterogeneity; Age and gender;
D O I
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中图分类号
学科分类号
摘要
This study investigated the demographic heterogeneity of COVID-19 infection to reveal the role of age structure and gender on COVID-19 diffusion patterns, demonstrating that the infection is distributed unevenly across ages, genders, and outbreak times. Based on cluster analysis, we analysed the 4-month COVID-19 outbreak data (N = 3017) in Thailand from January 12 to May 12, 2020, covering the early to late outbreak period of the initial wave. Results revealed that there are 7 pertinent clusters of COVID-19 outbreaks. Infection risk was classified by age, sex, and confirmed infection period. Results showed that elderly and young male clusters were at risk of becoming infected at the very beginning of the wave. Working-age male, young female, and elderly male clusters were key clusters controlling transmission when spreading became pervasive. Relevant clusters addressed at the end of the wave included general public and younger age clusters. Unlike other regions, the infection risk in Thailand is interestingly stronger among younger age clusters and male populations. Even though elderly individuals are at risk of becoming infected earlier than other clusters, the infection proportion was low. The findings provide new insights into the risk for COVID-19 infection.
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页码:567 / 588
页数:21
相关论文
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  • [1] Boehmer TK(2020)Changing Age Distribution of the COVID-19 Pandemic — United States, May–August 2020 Morbidity and Mortality Weekly Report 69 1404-1409
  • [2] DeVies J(2020)Demographic science aids in understanding the spread and fatality rates of COVID-19 Proceedings of the National Academy of Sciences of the United State of America 117 9696-9698
  • [3] Caruso E(2020)Monitoring trends and differences in COVID-19 case fatality rates using decomposition methods: Contributions of age structure and age-specific fatality PLoS ONE 15 1-10
  • [4] van Santen KL(2020)What are the underlying transmission patterns of COVID-19 Outbreak? An age-specific social contact characterization Eclinical Medicine 22 297-298
  • [5] Tang S(2020)Supporting young adults to rise to the challenge of COVID-19 Journal of Adolescent Health 67 21-41
  • [6] Black CL(1999)Intergenerational support and gender: A comparison of four Asian countries Asian Journal of Social Science 27 1067-1068
  • [7] Hartnett KP(2020)Journey of a Thai taxi driver and novel coronavirus The New England Journal of Medicine 382 669-677
  • [8] Kite-Powell A(2020)Estimates of the severity of coronavirus disease 2019: A model-based analysis The Lancet Infectious Diseases 20 236-244
  • [9] Dietz S(1963)Hierarchical grouping to optimize and objective function Journal of the American Statistical Association 58 undefined-undefined
  • [10] Lozier M(undefined)undefined undefined undefined undefined-undefined